{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "j0g0Dp8B5QGm"
      },
      "source": [
        "# Exercicio 2 - 🖼️ Classificação Imagens -> CIFAR-10"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TUBMPZG--Ylv"
      },
      "source": [
        "Considere uma rede deep learning convolutiva (treinada) aplicada à lassificação de padrões em imagens. A base de dados considerada é a CIFAR-10 (pesquise). A referida base de dados consiste de 60 mil imagens coloridas de 32x32 pixels, com 50 mil para treino e 10 mil para teste. As imagens estão divididas em 10 classes, a saber: avião, navio, caminhão, automóvel, sapo, pássaro, cachorro, gato, cavalo e cervo. Cada imagem possui apenas um dos objetos da classe de interesse, podendo estar parcialmente obstruído por outros objetos que não pertençam a esse conjunto. Apresente o desempenho da rede no processo de classificação usando uma matriz de confusão."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "44uStjG55GgC"
      },
      "outputs": [],
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow.keras.datasets import cifar10\n",
        "from tensorflow.keras.models import Sequential, load_model\n",
        "from tensorflow.keras.layers import Conv2D, MaxPooling2D, Dense, Dropout, Flatten, BatchNormalization, Input\n",
        "from keras.models import Model\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "from tensorflow.keras.utils import to_categorical\n",
        "from tensorflow.keras.callbacks import ModelCheckpoint, EarlyStopping\n",
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "from tensorflow.keras.applications import VGG16\n",
        "\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.metrics import confusion_matrix"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pnSx1BQq9ZLC"
      },
      "source": [
        "## Load the CIFAR-10 dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "P1Q6dQIY5iKm"
      },
      "outputs": [],
      "source": [
        "(x_train, y_train), (x_test, y_test) = cifar10.load_data()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cTVcHXsG_h0W",
        "outputId": "d7c53260-65cb-4655-adde-4fb1aa1661fa"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "x_train shape: (50000, 32, 32, 3)\n",
            "50000 train samples\n",
            "10000 test samples\n"
          ]
        }
      ],
      "source": [
        "# verify the shape\n",
        "print('x_train shape:', x_train.shape)\n",
        "print(x_train.shape[0], 'train samples')\n",
        "print(x_test.shape[0], 'test samples')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 826
        },
        "id": "rO39LM2p_u7M",
        "outputId": "ba678739-5088-434d-94f9-7e8adbb2b044"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 25 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# transformando em strings a numeração da classe\n",
        "class_names = ['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']\n",
        "\n",
        "plt.figure(figsize=(10, 10))\n",
        "for i in range(25):\n",
        "    plt.subplot(5, 5, i+1)\n",
        "    plt.xticks([])\n",
        "    plt.yticks([])\n",
        "    plt.grid(False)\n",
        "    plt.imshow(x_train[i], cmap=plt.cm.binary)\n",
        "    plt.xlabel(class_names[y_train[i][0]])\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kU71-F1y9qID",
        "outputId": "2eaa2cfc-2c4b-4ecc-fdee-1cd959bbcaf0"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dtype('uint8')"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ],
      "source": [
        "x_train.dtype"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "E21zKV6_98yu",
        "outputId": "1aeadf10-d5ad-4d3a-c39e-654de9b8b634"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dtype('uint8')"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ],
      "source": [
        "x_test.dtype"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9F_RHHfgaIsu"
      },
      "source": [
        "## Raw Model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4QfDebZb9gdD"
      },
      "source": [
        "### Preprocess the data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "ZtpE8DdI9dF7"
      },
      "outputs": [],
      "source": [
        "#transform data in float\n",
        "x_train = x_train.astype('float32')\n",
        "x_test = x_test.astype('float32')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "fkoauMvw9iux"
      },
      "outputs": [],
      "source": [
        "# Normalization Data.\n",
        "# By dividing by 255.0, you are scaling these pixel values to be between 0 and 1.\n",
        "x_train /= 255.0\n",
        "x_test /= 255.0"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "kmiYTQmx-GH8"
      },
      "outputs": [],
      "source": [
        "#transform y variable in categorical\n",
        "y_train = to_categorical(y_train)\n",
        "y_test = to_categorical(y_test)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oqfwwBIjA3Ie"
      },
      "source": [
        "### Define the model architecture"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "uxkpmR89AZNR",
        "outputId": "3fe3015a-4c06-4709-dc61-99f73f61b223",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.11/dist-packages/keras/src/layers/convolutional/base_conv.py:107: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
            "  super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"
          ]
        }
      ],
      "source": [
        "model = Sequential([\n",
        "    Conv2D(32, (3, 3), activation='relu', padding='same', input_shape=(32, 32, 3)),\n",
        "    BatchNormalization(),\n",
        "    Conv2D(32, (3, 3), activation='relu', padding='same'),\n",
        "    BatchNormalization(),\n",
        "    MaxPooling2D((2, 2)),\n",
        "    Dropout(0.2),\n",
        "\n",
        "    Conv2D(64, (3, 3), activation='relu', padding='same'),\n",
        "    BatchNormalization(),\n",
        "    Conv2D(64, (3, 3), activation='relu', padding='same'),\n",
        "    BatchNormalization(),\n",
        "    MaxPooling2D((2, 2)),\n",
        "    Dropout(0.3),\n",
        "\n",
        "    Conv2D(128, (3, 3), activation='relu', padding='same'),\n",
        "    BatchNormalization(),\n",
        "    Conv2D(128, (3, 3), activation='relu', padding='same'),\n",
        "    BatchNormalization(),\n",
        "    MaxPooling2D((2, 2)),\n",
        "    Dropout(0.4),\n",
        "\n",
        "    Flatten(),\n",
        "    Dense(128, activation='relu'),\n",
        "    BatchNormalization(),\n",
        "    Dropout(0.5),\n",
        "    Dense(10, activation='softmax')\n",
        "])\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.summary()"
      ],
      "metadata": {
        "id": "fXrZzutjFS93",
        "outputId": "2d1fa798-a16e-424b-9802-9c8d29f8f495",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1mModel: \"sequential\"\u001b[0m\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential\"</span>\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
              "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
              "│ conv2d (\u001b[38;5;33mConv2D\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m)     │           \u001b[38;5;34m896\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ batch_normalization             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m)     │           \u001b[38;5;34m128\u001b[0m │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)            │                        │               │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv2d_1 (\u001b[38;5;33mConv2D\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m)     │         \u001b[38;5;34m9,248\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ batch_normalization_1           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m)     │           \u001b[38;5;34m128\u001b[0m │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)            │                        │               │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ max_pooling2d (\u001b[38;5;33mMaxPooling2D\u001b[0m)    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m32\u001b[0m)     │             \u001b[38;5;34m0\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dropout (\u001b[38;5;33mDropout\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m32\u001b[0m)     │             \u001b[38;5;34m0\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv2d_2 (\u001b[38;5;33mConv2D\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m64\u001b[0m)     │        \u001b[38;5;34m18,496\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ batch_normalization_2           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m64\u001b[0m)     │           \u001b[38;5;34m256\u001b[0m │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)            │                        │               │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv2d_3 (\u001b[38;5;33mConv2D\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m64\u001b[0m)     │        \u001b[38;5;34m36,928\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ batch_normalization_3           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m64\u001b[0m)     │           \u001b[38;5;34m256\u001b[0m │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)            │                        │               │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ max_pooling2d_1 (\u001b[38;5;33mMaxPooling2D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m64\u001b[0m)       │             \u001b[38;5;34m0\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dropout_1 (\u001b[38;5;33mDropout\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m64\u001b[0m)       │             \u001b[38;5;34m0\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv2d_4 (\u001b[38;5;33mConv2D\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m128\u001b[0m)      │        \u001b[38;5;34m73,856\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ batch_normalization_4           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m128\u001b[0m)      │           \u001b[38;5;34m512\u001b[0m │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)            │                        │               │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv2d_5 (\u001b[38;5;33mConv2D\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m128\u001b[0m)      │       \u001b[38;5;34m147,584\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ batch_normalization_5           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m128\u001b[0m)      │           \u001b[38;5;34m512\u001b[0m │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)            │                        │               │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ max_pooling2d_2 (\u001b[38;5;33mMaxPooling2D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m128\u001b[0m)      │             \u001b[38;5;34m0\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dropout_2 (\u001b[38;5;33mDropout\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m128\u001b[0m)      │             \u001b[38;5;34m0\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ flatten (\u001b[38;5;33mFlatten\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2048\u001b[0m)           │             \u001b[38;5;34m0\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dense (\u001b[38;5;33mDense\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)            │       \u001b[38;5;34m262,272\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ batch_normalization_6           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)            │           \u001b[38;5;34m512\u001b[0m │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)            │                        │               │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dropout_3 (\u001b[38;5;33mDropout\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)            │             \u001b[38;5;34m0\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dense_1 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m)             │         \u001b[38;5;34m1,290\u001b[0m │\n",
              "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
              "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
              "│ conv2d (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)     │           <span style=\"color: #00af00; text-decoration-color: #00af00\">896</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ batch_normalization             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)     │           <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span> │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)            │                        │               │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv2d_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)     │         <span style=\"color: #00af00; text-decoration-color: #00af00\">9,248</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ batch_normalization_1           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)     │           <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span> │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)            │                        │               │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ max_pooling2d (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)     │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dropout (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)     │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv2d_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)     │        <span style=\"color: #00af00; text-decoration-color: #00af00\">18,496</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ batch_normalization_2           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)     │           <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span> │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)            │                        │               │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv2d_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)     │        <span style=\"color: #00af00; text-decoration-color: #00af00\">36,928</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ batch_normalization_3           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)     │           <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span> │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)            │                        │               │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ max_pooling2d_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)       │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dropout_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)       │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv2d_4 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)      │        <span style=\"color: #00af00; text-decoration-color: #00af00\">73,856</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ batch_normalization_4           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)      │           <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span> │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)            │                        │               │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv2d_5 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)      │       <span style=\"color: #00af00; text-decoration-color: #00af00\">147,584</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ batch_normalization_5           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)      │           <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span> │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)            │                        │               │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ max_pooling2d_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)      │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dropout_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)      │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ flatten (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Flatten</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2048</span>)           │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dense (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)            │       <span style=\"color: #00af00; text-decoration-color: #00af00\">262,272</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ batch_normalization_6           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)            │           <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span> │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)            │                        │               │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dropout_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)            │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dense_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">10</span>)             │         <span style=\"color: #00af00; text-decoration-color: #00af00\">1,290</span> │\n",
              "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m552,874\u001b[0m (2.11 MB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">552,874</span> (2.11 MB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m551,722\u001b[0m (2.10 MB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">551,722</span> (2.10 MB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m1,152\u001b[0m (4.50 KB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">1,152</span> (4.50 KB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ynPPOtssBM9n"
      },
      "source": [
        "### Compile the model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "sEYahglFA8mE"
      },
      "outputs": [],
      "source": [
        "model.compile(optimizer=Adam(learning_rate=0.001),\n",
        "              loss='categorical_crossentropy',\n",
        "              metrics=['accuracy'])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Yqdhs6UeYPwn"
      },
      "source": [
        "###  Callbacks"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "id": "HixGEL0RYNBC"
      },
      "outputs": [],
      "source": [
        "checkpoint = ModelCheckpoint('model.h5', monitor='val_accuracy', save_best_only=True, mode='max')\n",
        "early_stopping = EarlyStopping(monitor='val_loss', patience=10, restore_best_weights=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UKcj7Tp0BVRK"
      },
      "source": [
        "### Train the model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Y3sSGq1oBQCa",
        "outputId": "a7a8e85d-6cd1-432f-9f63-c2f05e3d0e20"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/50\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - accuracy: 0.3541 - loss: 2.0285"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m28s\u001b[0m 19ms/step - accuracy: 0.3542 - loss: 2.0280 - val_accuracy: 0.5803 - val_loss: 1.1975\n",
            "Epoch 2/50\n",
            "\u001b[1m780/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.5967 - loss: 1.1385"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m26s\u001b[0m 10ms/step - accuracy: 0.5968 - loss: 1.1383 - val_accuracy: 0.6916 - val_loss: 0.8819\n",
            "Epoch 3/50\n",
            "\u001b[1m776/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.6863 - loss: 0.8955"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 9ms/step - accuracy: 0.6863 - loss: 0.8953 - val_accuracy: 0.7389 - val_loss: 0.7435\n",
            "Epoch 4/50\n",
            "\u001b[1m777/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.7339 - loss: 0.7766"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 9ms/step - accuracy: 0.7339 - loss: 0.7765 - val_accuracy: 0.7409 - val_loss: 0.7851\n",
            "Epoch 5/50\n",
            "\u001b[1m775/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.7515 - loss: 0.7125"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 9ms/step - accuracy: 0.7515 - loss: 0.7125 - val_accuracy: 0.7512 - val_loss: 0.7089\n",
            "Epoch 6/50\n",
            "\u001b[1m775/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.7782 - loss: 0.6470"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 9ms/step - accuracy: 0.7782 - loss: 0.6470 - val_accuracy: 0.7729 - val_loss: 0.6496\n",
            "Epoch 7/50\n",
            "\u001b[1m776/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.7979 - loss: 0.5905"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 9ms/step - accuracy: 0.7978 - loss: 0.5906 - val_accuracy: 0.7922 - val_loss: 0.6218\n",
            "Epoch 8/50\n",
            "\u001b[1m776/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.8061 - loss: 0.5613"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 9ms/step - accuracy: 0.8061 - loss: 0.5613 - val_accuracy: 0.8039 - val_loss: 0.5785\n",
            "Epoch 9/50\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 9ms/step - accuracy: 0.8261 - loss: 0.5187 - val_accuracy: 0.7878 - val_loss: 0.6288\n",
            "Epoch 10/50\n",
            "\u001b[1m780/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.8314 - loss: 0.4913"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 9ms/step - accuracy: 0.8314 - loss: 0.4913 - val_accuracy: 0.8166 - val_loss: 0.5327\n",
            "Epoch 11/50\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 9ms/step - accuracy: 0.8382 - loss: 0.4712 - val_accuracy: 0.8123 - val_loss: 0.5694\n",
            "Epoch 12/50\n",
            "\u001b[1m778/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.8482 - loss: 0.4411"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 9ms/step - accuracy: 0.8482 - loss: 0.4412 - val_accuracy: 0.8356 - val_loss: 0.5037\n",
            "Epoch 13/50\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 9ms/step - accuracy: 0.8549 - loss: 0.4222 - val_accuracy: 0.8198 - val_loss: 0.5409\n",
            "Epoch 14/50\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 8ms/step - accuracy: 0.8610 - loss: 0.4041 - val_accuracy: 0.8070 - val_loss: 0.5904\n",
            "Epoch 15/50\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 9ms/step - accuracy: 0.8663 - loss: 0.3922 - val_accuracy: 0.8249 - val_loss: 0.5346\n",
            "Epoch 16/50\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 9ms/step - accuracy: 0.8728 - loss: 0.3754 - val_accuracy: 0.8298 - val_loss: 0.5178\n",
            "Epoch 17/50\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 9ms/step - accuracy: 0.8766 - loss: 0.3609 - val_accuracy: 0.8053 - val_loss: 0.6058\n",
            "Epoch 18/50\n",
            "\u001b[1m780/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.8805 - loss: 0.3430"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 9ms/step - accuracy: 0.8805 - loss: 0.3431 - val_accuracy: 0.8422 - val_loss: 0.4688\n",
            "Epoch 19/50\n",
            "\u001b[1m781/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.8836 - loss: 0.3401"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 9ms/step - accuracy: 0.8835 - loss: 0.3401 - val_accuracy: 0.8476 - val_loss: 0.4700\n",
            "Epoch 20/50\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 9ms/step - accuracy: 0.8884 - loss: 0.3261 - val_accuracy: 0.8404 - val_loss: 0.5064\n",
            "Epoch 21/50\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.8897 - loss: 0.3242"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 9ms/step - accuracy: 0.8897 - loss: 0.3242 - val_accuracy: 0.8479 - val_loss: 0.4797\n",
            "Epoch 22/50\n",
            "\u001b[1m777/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.8941 - loss: 0.3047"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 9ms/step - accuracy: 0.8941 - loss: 0.3048 - val_accuracy: 0.8531 - val_loss: 0.4712\n",
            "Epoch 23/50\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 8ms/step - accuracy: 0.8926 - loss: 0.3082 - val_accuracy: 0.8527 - val_loss: 0.4768\n",
            "Epoch 24/50\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 9ms/step - accuracy: 0.9011 - loss: 0.2875 - val_accuracy: 0.8504 - val_loss: 0.4701\n",
            "Epoch 25/50\n",
            "\u001b[1m775/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.8998 - loss: 0.2833"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 9ms/step - accuracy: 0.8998 - loss: 0.2834 - val_accuracy: 0.8544 - val_loss: 0.4723\n",
            "Epoch 26/50\n",
            "\u001b[1m781/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.8993 - loss: 0.2875"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 9ms/step - accuracy: 0.8993 - loss: 0.2875 - val_accuracy: 0.8580 - val_loss: 0.4613\n",
            "Epoch 27/50\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 9ms/step - accuracy: 0.9037 - loss: 0.2743 - val_accuracy: 0.8548 - val_loss: 0.4648\n",
            "Epoch 28/50\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 9ms/step - accuracy: 0.9067 - loss: 0.2724 - val_accuracy: 0.8579 - val_loss: 0.4556\n",
            "Epoch 29/50\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 9ms/step - accuracy: 0.9069 - loss: 0.2626 - val_accuracy: 0.8482 - val_loss: 0.4934\n",
            "Epoch 30/50\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 9ms/step - accuracy: 0.9092 - loss: 0.2586 - val_accuracy: 0.8575 - val_loss: 0.4787\n",
            "Epoch 31/50\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 9ms/step - accuracy: 0.9117 - loss: 0.2555 - val_accuracy: 0.8540 - val_loss: 0.4764\n",
            "Epoch 32/50\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 8ms/step - accuracy: 0.9130 - loss: 0.2490 - val_accuracy: 0.8546 - val_loss: 0.5066\n",
            "Epoch 33/50\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 9ms/step - accuracy: 0.9154 - loss: 0.2452 - val_accuracy: 0.8576 - val_loss: 0.4697\n",
            "Epoch 34/50\n",
            "\u001b[1m779/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.9177 - loss: 0.2369"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 9ms/step - accuracy: 0.9177 - loss: 0.2369 - val_accuracy: 0.8618 - val_loss: 0.4588\n",
            "Epoch 35/50\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 9ms/step - accuracy: 0.9196 - loss: 0.2331 - val_accuracy: 0.8618 - val_loss: 0.4671\n",
            "Epoch 36/50\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 9ms/step - accuracy: 0.9217 - loss: 0.2241 - val_accuracy: 0.8506 - val_loss: 0.5150\n",
            "Epoch 37/50\n",
            "\u001b[1m778/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.9212 - loss: 0.2252"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 9ms/step - accuracy: 0.9212 - loss: 0.2252 - val_accuracy: 0.8675 - val_loss: 0.4469\n",
            "Epoch 38/50\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 9ms/step - accuracy: 0.9228 - loss: 0.2255 - val_accuracy: 0.8608 - val_loss: 0.4664\n",
            "Epoch 39/50\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 9ms/step - accuracy: 0.9215 - loss: 0.2193 - val_accuracy: 0.8675 - val_loss: 0.4558\n",
            "Epoch 40/50\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 9ms/step - accuracy: 0.9258 - loss: 0.2105 - val_accuracy: 0.8620 - val_loss: 0.4765\n",
            "Epoch 41/50\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 9ms/step - accuracy: 0.9264 - loss: 0.2092 - val_accuracy: 0.8613 - val_loss: 0.4713\n",
            "Epoch 42/50\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 9ms/step - accuracy: 0.9243 - loss: 0.2171 - val_accuracy: 0.8632 - val_loss: 0.4799\n",
            "Epoch 43/50\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 9ms/step - accuracy: 0.9304 - loss: 0.2038 - val_accuracy: 0.8579 - val_loss: 0.4915\n",
            "Epoch 44/50\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 9ms/step - accuracy: 0.9303 - loss: 0.1990 - val_accuracy: 0.8649 - val_loss: 0.4641\n",
            "Epoch 45/50\n",
            "\u001b[1m780/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.9291 - loss: 0.2062"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 9ms/step - accuracy: 0.9291 - loss: 0.2062 - val_accuracy: 0.8683 - val_loss: 0.4519\n",
            "Epoch 46/50\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 9ms/step - accuracy: 0.9322 - loss: 0.1942 - val_accuracy: 0.8654 - val_loss: 0.4751\n",
            "Epoch 47/50\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 9ms/step - accuracy: 0.9332 - loss: 0.1926 - val_accuracy: 0.8654 - val_loss: 0.4632\n"
          ]
        }
      ],
      "source": [
        "history = model.fit(x_train, y_train,\n",
        "                    validation_data=(x_test, y_test),\n",
        "                    epochs=50,\n",
        "                    batch_size=64,\n",
        "                    callbacks=[checkpoint, early_stopping])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "N1_aXN61BcZF"
      },
      "source": [
        "### Evaluate the model on the test set"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mxviTIPSBbjP",
        "outputId": "c3eb430a-b7ad-4170-bade-9d7660c4adb4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "313/313 - 2s - 5ms/step - accuracy: 0.8675 - loss: 0.4469\n",
            "\n",
            "Test accuracy: 0.8675000071525574\n"
          ]
        }
      ],
      "source": [
        "test_loss, test_acc = model.evaluate(x_test, y_test, verbose=2)\n",
        "print('\\nTest accuracy:', test_acc)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 494
        },
        "id": "H7XVX1UmhPkJ",
        "outputId": "89ae6781-f8a5-4107-8e68-3be4984634d7"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.figure(figsize=(10, 5))\n",
        "plt.plot(history.history['accuracy'], label='Train Accuracy')\n",
        "plt.plot(history.history['val_accuracy'], label='Validation Accuracy')\n",
        "plt.xlabel('Epochs', fontsize=14)\n",
        "plt.ylabel('Accuracy', fontsize=14)\n",
        "plt.title('Accuracy and Validation Accuracy', fontsize=16)\n",
        "plt.legend()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0WRSCIk_BiDA"
      },
      "source": [
        "### Make predictions on the test set"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zwlMvQoLBYBG",
        "outputId": "d50412d9-821f-4ea7-f2f4-9382fe884f57"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 5ms/step\n"
          ]
        }
      ],
      "source": [
        "y_pred = model.predict(x_test)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CVB5bLIYBlRc"
      },
      "source": [
        "### Convert predictions from one-hot encoding to class labels"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "id": "V2l8kZwYBskR"
      },
      "outputs": [],
      "source": [
        "y_pred_classes = np.argmax(y_pred, axis=1)\n",
        "y_true_classes = np.argmax(y_test, axis=1)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OCA6qlg5B1Ap"
      },
      "source": [
        "### Create and Print the confusion matrix"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cm = confusion_matrix(y_true_classes, y_pred_classes)"
      ],
      "metadata": {
        "id": "WSHJG1LgJSIY"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.figure(figsize=(10, 10))\n",
        "sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', xticklabels=class_names, yticklabels=class_names, cbar=False)\n",
        "plt.xlabel('Predicted', fontsize=14)\n",
        "plt.ylabel('Actual', fontsize=14)\n",
        "plt.title('Confusion Matrix', fontsize=16)\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "ui8eRbynJfW4",
        "outputId": "dd11cd5f-dc05-4a48-f246-722410b6374f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 879
        }
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "onPeuARtaSyd"
      },
      "source": [
        "## Transfer Learning from DenseNet201"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7P8AZ8qA6y6Z"
      },
      "source": [
        "### Define the model architecture"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 795
        },
        "id": "GERGS4Nmacgc",
        "outputId": "2ebb17d0-12fe-410d-c4fc-6079b3108312"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/vgg16/vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
            "\u001b[1m58889256/58889256\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0us/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1mModel: \"vgg16\"\u001b[0m\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"vgg16\"</span>\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
              "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
              "│ input_layer_1 (\u001b[38;5;33mInputLayer\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m3\u001b[0m)      │             \u001b[38;5;34m0\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ block1_conv1 (\u001b[38;5;33mConv2D\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m64\u001b[0m)     │         \u001b[38;5;34m1,792\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ block1_conv2 (\u001b[38;5;33mConv2D\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m64\u001b[0m)     │        \u001b[38;5;34m36,928\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ block1_pool (\u001b[38;5;33mMaxPooling2D\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m64\u001b[0m)     │             \u001b[38;5;34m0\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ block2_conv1 (\u001b[38;5;33mConv2D\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m128\u001b[0m)    │        \u001b[38;5;34m73,856\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ block2_conv2 (\u001b[38;5;33mConv2D\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m128\u001b[0m)    │       \u001b[38;5;34m147,584\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ block2_pool (\u001b[38;5;33mMaxPooling2D\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m128\u001b[0m)      │             \u001b[38;5;34m0\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ block3_conv1 (\u001b[38;5;33mConv2D\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m256\u001b[0m)      │       \u001b[38;5;34m295,168\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ block3_conv2 (\u001b[38;5;33mConv2D\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m256\u001b[0m)      │       \u001b[38;5;34m590,080\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ block3_conv3 (\u001b[38;5;33mConv2D\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m256\u001b[0m)      │       \u001b[38;5;34m590,080\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ block3_pool (\u001b[38;5;33mMaxPooling2D\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m256\u001b[0m)      │             \u001b[38;5;34m0\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ block4_conv1 (\u001b[38;5;33mConv2D\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m512\u001b[0m)      │     \u001b[38;5;34m1,180,160\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ block4_conv2 (\u001b[38;5;33mConv2D\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m512\u001b[0m)      │     \u001b[38;5;34m2,359,808\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ block4_conv3 (\u001b[38;5;33mConv2D\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m512\u001b[0m)      │     \u001b[38;5;34m2,359,808\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ block4_pool (\u001b[38;5;33mMaxPooling2D\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2\u001b[0m, \u001b[38;5;34m2\u001b[0m, \u001b[38;5;34m512\u001b[0m)      │             \u001b[38;5;34m0\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ block5_conv1 (\u001b[38;5;33mConv2D\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2\u001b[0m, \u001b[38;5;34m2\u001b[0m, \u001b[38;5;34m512\u001b[0m)      │     \u001b[38;5;34m2,359,808\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ block5_conv2 (\u001b[38;5;33mConv2D\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2\u001b[0m, \u001b[38;5;34m2\u001b[0m, \u001b[38;5;34m512\u001b[0m)      │     \u001b[38;5;34m2,359,808\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ block5_conv3 (\u001b[38;5;33mConv2D\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2\u001b[0m, \u001b[38;5;34m2\u001b[0m, \u001b[38;5;34m512\u001b[0m)      │     \u001b[38;5;34m2,359,808\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ block5_pool (\u001b[38;5;33mMaxPooling2D\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m512\u001b[0m)      │             \u001b[38;5;34m0\u001b[0m │\n",
              "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
              "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
              "│ input_layer_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">InputLayer</span>)      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>)      │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ block1_conv1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)     │         <span style=\"color: #00af00; text-decoration-color: #00af00\">1,792</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ block1_conv2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)     │        <span style=\"color: #00af00; text-decoration-color: #00af00\">36,928</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ block1_pool (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)     │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ block2_conv1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)    │        <span style=\"color: #00af00; text-decoration-color: #00af00\">73,856</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ block2_conv2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)    │       <span style=\"color: #00af00; text-decoration-color: #00af00\">147,584</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ block2_pool (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)      │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ block3_conv1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)      │       <span style=\"color: #00af00; text-decoration-color: #00af00\">295,168</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ block3_conv2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)      │       <span style=\"color: #00af00; text-decoration-color: #00af00\">590,080</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ block3_conv3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)      │       <span style=\"color: #00af00; text-decoration-color: #00af00\">590,080</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ block3_pool (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)      │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ block4_conv1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)      │     <span style=\"color: #00af00; text-decoration-color: #00af00\">1,180,160</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ block4_conv2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)      │     <span style=\"color: #00af00; text-decoration-color: #00af00\">2,359,808</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ block4_conv3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)      │     <span style=\"color: #00af00; text-decoration-color: #00af00\">2,359,808</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ block4_pool (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)      │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ block5_conv1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)      │     <span style=\"color: #00af00; text-decoration-color: #00af00\">2,359,808</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ block5_conv2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)      │     <span style=\"color: #00af00; text-decoration-color: #00af00\">2,359,808</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ block5_conv3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)      │     <span style=\"color: #00af00; text-decoration-color: #00af00\">2,359,808</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ block5_pool (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)      │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m14,714,688\u001b[0m (56.13 MB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">14,714,688</span> (56.13 MB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m14,714,688\u001b[0m (56.13 MB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">14,714,688</span> (56.13 MB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "# instantiate the model\n",
        "vgg16 = VGG16(\n",
        "      include_top=False,\n",
        "      weights=\"imagenet\",\n",
        "      input_shape=(32, 32, 3),\n",
        ")\n",
        "\n",
        "vgg16.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 941
        },
        "id": "gmDXWB5ebFRp",
        "outputId": "897e5785-6bed-4fe9-ee37-c01a03e83202"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1mModel: \"functional_1\"\u001b[0m\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"functional_1\"</span>\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
              "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
              "│ input_layer_1 (\u001b[38;5;33mInputLayer\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m3\u001b[0m)      │             \u001b[38;5;34m0\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ block1_conv1 (\u001b[38;5;33mConv2D\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m64\u001b[0m)     │         \u001b[38;5;34m1,792\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ block1_conv2 (\u001b[38;5;33mConv2D\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m64\u001b[0m)     │        \u001b[38;5;34m36,928\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ block1_pool (\u001b[38;5;33mMaxPooling2D\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m64\u001b[0m)     │             \u001b[38;5;34m0\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ block2_conv1 (\u001b[38;5;33mConv2D\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m128\u001b[0m)    │        \u001b[38;5;34m73,856\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ block2_conv2 (\u001b[38;5;33mConv2D\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m128\u001b[0m)    │       \u001b[38;5;34m147,584\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ block2_pool (\u001b[38;5;33mMaxPooling2D\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m128\u001b[0m)      │             \u001b[38;5;34m0\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ block3_conv1 (\u001b[38;5;33mConv2D\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m256\u001b[0m)      │       \u001b[38;5;34m295,168\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ block3_conv2 (\u001b[38;5;33mConv2D\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m256\u001b[0m)      │       \u001b[38;5;34m590,080\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ block3_conv3 (\u001b[38;5;33mConv2D\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m256\u001b[0m)      │       \u001b[38;5;34m590,080\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ block3_pool (\u001b[38;5;33mMaxPooling2D\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m256\u001b[0m)      │             \u001b[38;5;34m0\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ block4_conv1 (\u001b[38;5;33mConv2D\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m512\u001b[0m)      │     \u001b[38;5;34m1,180,160\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ block4_conv2 (\u001b[38;5;33mConv2D\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m512\u001b[0m)      │     \u001b[38;5;34m2,359,808\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ block4_conv3 (\u001b[38;5;33mConv2D\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m512\u001b[0m)      │     \u001b[38;5;34m2,359,808\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ block4_pool (\u001b[38;5;33mMaxPooling2D\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2\u001b[0m, \u001b[38;5;34m2\u001b[0m, \u001b[38;5;34m512\u001b[0m)      │             \u001b[38;5;34m0\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ block5_conv1 (\u001b[38;5;33mConv2D\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2\u001b[0m, \u001b[38;5;34m2\u001b[0m, \u001b[38;5;34m512\u001b[0m)      │     \u001b[38;5;34m2,359,808\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ block5_conv2 (\u001b[38;5;33mConv2D\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2\u001b[0m, \u001b[38;5;34m2\u001b[0m, \u001b[38;5;34m512\u001b[0m)      │     \u001b[38;5;34m2,359,808\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ block5_conv3 (\u001b[38;5;33mConv2D\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2\u001b[0m, \u001b[38;5;34m2\u001b[0m, \u001b[38;5;34m512\u001b[0m)      │     \u001b[38;5;34m2,359,808\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ block5_pool (\u001b[38;5;33mMaxPooling2D\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m512\u001b[0m)      │             \u001b[38;5;34m0\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ flatten_1 (\u001b[38;5;33mFlatten\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m512\u001b[0m)            │             \u001b[38;5;34m0\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dense_2 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)            │        \u001b[38;5;34m65,664\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ batch_normalization_7           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)            │           \u001b[38;5;34m512\u001b[0m │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)            │                        │               │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dropout_4 (\u001b[38;5;33mDropout\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)            │             \u001b[38;5;34m0\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dense_3 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m)             │         \u001b[38;5;34m1,290\u001b[0m │\n",
              "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
              "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
              "│ input_layer_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">InputLayer</span>)      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>)      │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ block1_conv1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)     │         <span style=\"color: #00af00; text-decoration-color: #00af00\">1,792</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ block1_conv2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)     │        <span style=\"color: #00af00; text-decoration-color: #00af00\">36,928</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ block1_pool (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)     │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ block2_conv1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)    │        <span style=\"color: #00af00; text-decoration-color: #00af00\">73,856</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ block2_conv2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)    │       <span style=\"color: #00af00; text-decoration-color: #00af00\">147,584</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ block2_pool (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)      │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ block3_conv1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)      │       <span style=\"color: #00af00; text-decoration-color: #00af00\">295,168</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ block3_conv2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)      │       <span style=\"color: #00af00; text-decoration-color: #00af00\">590,080</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ block3_conv3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)      │       <span style=\"color: #00af00; text-decoration-color: #00af00\">590,080</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ block3_pool (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)      │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ block4_conv1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)      │     <span style=\"color: #00af00; text-decoration-color: #00af00\">1,180,160</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ block4_conv2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)      │     <span style=\"color: #00af00; text-decoration-color: #00af00\">2,359,808</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ block4_conv3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)      │     <span style=\"color: #00af00; text-decoration-color: #00af00\">2,359,808</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ block4_pool (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)      │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ block5_conv1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)      │     <span style=\"color: #00af00; text-decoration-color: #00af00\">2,359,808</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ block5_conv2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)      │     <span style=\"color: #00af00; text-decoration-color: #00af00\">2,359,808</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ block5_conv3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)      │     <span style=\"color: #00af00; text-decoration-color: #00af00\">2,359,808</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ block5_pool (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)      │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ flatten_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Flatten</span>)             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)            │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dense_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)            │        <span style=\"color: #00af00; text-decoration-color: #00af00\">65,664</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ batch_normalization_7           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)            │           <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span> │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)            │                        │               │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dropout_4 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)            │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dense_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">10</span>)             │         <span style=\"color: #00af00; text-decoration-color: #00af00\">1,290</span> │\n",
              "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m14,782,154\u001b[0m (56.39 MB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">14,782,154</span> (56.39 MB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m67,210\u001b[0m (262.54 KB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">67,210</span> (262.54 KB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m14,714,944\u001b[0m (56.13 MB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">14,714,944</span> (56.13 MB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Congela os pesos das camadas do modelo base\n",
        "for layer in vgg16.layers:\n",
        "    layer.trainable = False\n",
        "\n",
        "# Adiciona as camadas personalizadas ao modelo\n",
        "x = Flatten()(vgg16.output)\n",
        "x = Dense(128, activation='relu')(x)\n",
        "x = BatchNormalization()(x)\n",
        "x = Dropout(0.5)(x)\n",
        "output = Dense(10, activation='softmax')(x)\n",
        "\n",
        "# Cria o novo modelo com as camadas personalizadas\n",
        "model = Model(inputs=vgg16.input, outputs=output)\n",
        "\n",
        "# Visualiza a arquitetura do modelo\n",
        "model.summary()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4FItCLLX6_Tb"
      },
      "source": [
        "### Compile the model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {
        "id": "_Z_Fvsgfca_8"
      },
      "outputs": [],
      "source": [
        "# compile the model\n",
        "model.compile(optimizer=Adam(learning_rate=0.001),\n",
        "              loss='categorical_crossentropy',\n",
        "              metrics=['accuracy'])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rkOEKhnL7HLJ"
      },
      "source": [
        "### Callbacks"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "metadata": {
        "id": "PzNeulFKcsPu"
      },
      "outputs": [],
      "source": [
        "checkpoint = ModelCheckpoint('model.h5', monitor='val_accuracy', save_best_only=True, mode='max')\n",
        "early_stopping = EarlyStopping(monitor='val_loss', patience=10, restore_best_weights=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XRS5uzv17JOe"
      },
      "source": [
        "### Train the model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Zf2I4zavcwl9",
        "outputId": "17ff7992-5788-4be4-ab30-2003a89f6ff4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/50\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - accuracy: 0.3891 - loss: 1.8381"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m22s\u001b[0m 22ms/step - accuracy: 0.3892 - loss: 1.8378 - val_accuracy: 0.5517 - val_loss: 1.2807\n",
            "Epoch 2/50\n",
            "\u001b[1m779/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - accuracy: 0.5382 - loss: 1.3356"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 15ms/step - accuracy: 0.5382 - loss: 1.3356 - val_accuracy: 0.5687 - val_loss: 1.2360\n",
            "Epoch 3/50\n",
            "\u001b[1m778/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - accuracy: 0.5546 - loss: 1.2890"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 16ms/step - accuracy: 0.5547 - loss: 1.2890 - val_accuracy: 0.5761 - val_loss: 1.2090\n",
            "Epoch 4/50\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 14ms/step - accuracy: 0.5639 - loss: 1.2623 - val_accuracy: 0.5659 - val_loss: 1.2246\n",
            "Epoch 5/50\n",
            "\u001b[1m780/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - accuracy: 0.5717 - loss: 1.2429"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 15ms/step - accuracy: 0.5717 - loss: 1.2429 - val_accuracy: 0.5845 - val_loss: 1.1935\n",
            "Epoch 6/50\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 14ms/step - accuracy: 0.5724 - loss: 1.2350 - val_accuracy: 0.5783 - val_loss: 1.2037\n",
            "Epoch 7/50\n",
            "\u001b[1m778/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - accuracy: 0.5798 - loss: 1.2176"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 15ms/step - accuracy: 0.5798 - loss: 1.2177 - val_accuracy: 0.5878 - val_loss: 1.1805\n",
            "Epoch 8/50\n",
            "\u001b[1m780/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - accuracy: 0.5798 - loss: 1.2201"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 14ms/step - accuracy: 0.5798 - loss: 1.2201 - val_accuracy: 0.5881 - val_loss: 1.1827\n",
            "Epoch 9/50\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 14ms/step - accuracy: 0.5849 - loss: 1.2093 - val_accuracy: 0.5792 - val_loss: 1.1938\n",
            "Epoch 10/50\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 14ms/step - accuracy: 0.5833 - loss: 1.2001 - val_accuracy: 0.5869 - val_loss: 1.1795\n",
            "Epoch 11/50\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 14ms/step - accuracy: 0.5895 - loss: 1.1897 - val_accuracy: 0.5869 - val_loss: 1.1804\n",
            "Epoch 12/50\n",
            "\u001b[1m781/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - accuracy: 0.5912 - loss: 1.1831"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 16ms/step - accuracy: 0.5912 - loss: 1.1831 - val_accuracy: 0.5936 - val_loss: 1.1698\n",
            "Epoch 13/50\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 15ms/step - accuracy: 0.5908 - loss: 1.1870 - val_accuracy: 0.5916 - val_loss: 1.1626\n",
            "Epoch 14/50\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 14ms/step - accuracy: 0.5945 - loss: 1.1757 - val_accuracy: 0.5906 - val_loss: 1.1719\n",
            "Epoch 15/50\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 14ms/step - accuracy: 0.5922 - loss: 1.1814 - val_accuracy: 0.5923 - val_loss: 1.1678\n",
            "Epoch 16/50\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 14ms/step - accuracy: 0.5936 - loss: 1.1778 - val_accuracy: 0.5935 - val_loss: 1.1692\n",
            "Epoch 17/50\n",
            "\u001b[1m780/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - accuracy: 0.5954 - loss: 1.1721"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 14ms/step - accuracy: 0.5954 - loss: 1.1722 - val_accuracy: 0.5982 - val_loss: 1.1519\n",
            "Epoch 18/50\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 14ms/step - accuracy: 0.5962 - loss: 1.1680 - val_accuracy: 0.5969 - val_loss: 1.1556\n",
            "Epoch 19/50\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 15ms/step - accuracy: 0.5923 - loss: 1.1735 - val_accuracy: 0.5864 - val_loss: 1.1764\n",
            "Epoch 20/50\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 14ms/step - accuracy: 0.5966 - loss: 1.1687 - val_accuracy: 0.5973 - val_loss: 1.1484\n",
            "Epoch 21/50\n",
            "\u001b[1m778/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - accuracy: 0.6014 - loss: 1.1574"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 14ms/step - accuracy: 0.6013 - loss: 1.1574 - val_accuracy: 0.5997 - val_loss: 1.1532\n",
            "Epoch 22/50\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 14ms/step - accuracy: 0.5989 - loss: 1.1643 - val_accuracy: 0.5950 - val_loss: 1.1507\n",
            "Epoch 23/50\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 14ms/step - accuracy: 0.5988 - loss: 1.1586 - val_accuracy: 0.5982 - val_loss: 1.1511\n",
            "Epoch 24/50\n",
            "\u001b[1m778/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - accuracy: 0.6046 - loss: 1.1473"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 15ms/step - accuracy: 0.6045 - loss: 1.1473 - val_accuracy: 0.6005 - val_loss: 1.1463\n",
            "Epoch 25/50\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 14ms/step - accuracy: 0.6063 - loss: 1.1454 - val_accuracy: 0.5953 - val_loss: 1.1533\n",
            "Epoch 26/50\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 14ms/step - accuracy: 0.6060 - loss: 1.1422 - val_accuracy: 0.5937 - val_loss: 1.1505\n",
            "Epoch 27/50\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 14ms/step - accuracy: 0.6052 - loss: 1.1414 - val_accuracy: 0.6005 - val_loss: 1.1417\n",
            "Epoch 28/50\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 14ms/step - accuracy: 0.6046 - loss: 1.1431 - val_accuracy: 0.5970 - val_loss: 1.1435\n",
            "Epoch 29/50\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 14ms/step - accuracy: 0.6078 - loss: 1.1303 - val_accuracy: 0.5981 - val_loss: 1.1493\n",
            "Epoch 30/50\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 15ms/step - accuracy: 0.6063 - loss: 1.1416 - val_accuracy: 0.6000 - val_loss: 1.1471\n",
            "Epoch 31/50\n",
            "\u001b[1m779/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - accuracy: 0.6122 - loss: 1.1225"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 14ms/step - accuracy: 0.6122 - loss: 1.1226 - val_accuracy: 0.6022 - val_loss: 1.1419\n",
            "Epoch 32/50\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 15ms/step - accuracy: 0.6088 - loss: 1.1238 - val_accuracy: 0.6007 - val_loss: 1.1424\n",
            "Epoch 33/50\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 14ms/step - accuracy: 0.6051 - loss: 1.1389 - val_accuracy: 0.5973 - val_loss: 1.1467\n",
            "Epoch 34/50\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 15ms/step - accuracy: 0.6071 - loss: 1.1277 - val_accuracy: 0.5980 - val_loss: 1.1481\n",
            "Epoch 35/50\n",
            "\u001b[1m781/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - accuracy: 0.6088 - loss: 1.1285"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 15ms/step - accuracy: 0.6088 - loss: 1.1285 - val_accuracy: 0.6024 - val_loss: 1.1418\n",
            "Epoch 36/50\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 14ms/step - accuracy: 0.6078 - loss: 1.1269 - val_accuracy: 0.6005 - val_loss: 1.1438\n",
            "Epoch 37/50\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 15ms/step - accuracy: 0.6099 - loss: 1.1249 - val_accuracy: 0.6013 - val_loss: 1.1464\n"
          ]
        }
      ],
      "source": [
        "history = model.fit(x_train, y_train,\n",
        "                    validation_data=(x_test, y_test),\n",
        "                    epochs=50,\n",
        "                    batch_size=64,\n",
        "                    callbacks=[checkpoint, early_stopping])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RBVRxEq27PIJ"
      },
      "source": [
        "### Evaluate the model on test set"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WtAM0pHbc5CA",
        "outputId": "4986e2a6-7163-47c4-b99c-77e28880f620"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "313/313 - 4s - 13ms/step - accuracy: 0.6005 - loss: 1.1417\n",
            "\n",
            "Test accuracy: 0.6004999876022339\n"
          ]
        }
      ],
      "source": [
        "test_loss, test_acc = model.evaluate(x_test, y_test, verbose=2)\n",
        "print('\\nTest accuracy:', test_acc)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 494
        },
        "id": "fhSCpLMOc77q",
        "outputId": "bd1b6613-30d5-456a-9eab-ec14996a0679"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.figure(figsize=(10, 5))\n",
        "plt.plot(history.history['accuracy'], label='Train Accuracy')\n",
        "plt.plot(history.history['val_accuracy'], label='Validation Accuracy')\n",
        "plt.xlabel('Epochs', fontsize=14)\n",
        "plt.ylabel('Accuracy', fontsize=14)\n",
        "plt.title('Accuracy and Validation Accuracy', fontsize=16)\n",
        "plt.legend()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7oOXJlUL7i2m"
      },
      "source": [
        "### Make predictions on the test set"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CU01_iwtc-xi",
        "outputId": "0751d191-d6f8-4d95-ebe1-2039e113618b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 9ms/step\n"
          ]
        }
      ],
      "source": [
        "y_pred = model.predict(x_test)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "chguRImO7d3y"
      },
      "source": [
        "### Convert predictions from one-hot encoding to class labels"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 45,
      "metadata": {
        "id": "UO_1NcNYdA-6"
      },
      "outputs": [],
      "source": [
        "y_pred_classes = np.argmax(y_pred, axis=1)\n",
        "y_true_classes = np.argmax(y_test, axis=1)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yj-TFQU57SFN"
      },
      "source": [
        "### Create and Print confusion matrix"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 47,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 879
        },
        "id": "QPSIo3rTdElq",
        "outputId": "f893f742-0ffb-449c-e952-67c68eabdd93"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "cm = confusion_matrix(y_true_classes, y_pred_classes)\n",
        "\n",
        "plt.figure(figsize=(10, 10))\n",
        "sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', xticklabels=class_names, yticklabels=class_names, cbar=False)\n",
        "plt.xlabel('Predicted', fontsize=14)\n",
        "plt.ylabel('Actual', fontsize=14)\n",
        "plt.title('Confusion Matrix', fontsize=16)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eCcpBifJKtwZ"
      },
      "source": [
        "A análise dos resultados revela que o modelo que utilizou transfer learning com a rede VGG16 teve um desempenho inferior ao modelo treinado do zero. Foram observadas, por exemplo, confusões frequentes entre as classes \"carro\" e \"avião\", assim como entre \"cachorro\" e \"gato\", entre outras inconsistências. No entanto, embora este resultado específico indique limitações, é importante ressaltar que, de forma geral, a técnica de transfer learning tende a apresentar vantagens em relação aos modelos treinados do zero, sobretudo ao ser aplicada em diferentes contextos e conjuntos de dados.\n"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "gpuType": "T4",
      "provenance": []
    },
    "kernelspec": {
      "display_name": ".venv",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.9.6"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}